Data Lead - Azure Data Factory; ADF), Databricks

Position: Data Lead - Azure Data Factory (ADF), Databricks
Data Integration:
Integrate structured and unstructured data from multiple data sources into a unified data system, ensuring data quality and consistency.

Data Processing:
Implement data processing frameworks e.g., Hadoop, Spark to process large datasets in real-time or batch processing.

Automation and Monitoring:
Automate manual processes, optimize data delivery, and develop data monitoring systems to ensure data integrity and accuracy.

Collaboration:

Work closely with data scientists, analysts, and other stakeholders to understand data needs and provide technical solutions that meet business requirements.

Data Governance:
Ensure data governance policies are followed, including data security, data privacy, and compliance with regulations.

Performance Tuning:
Optimize the performance of ETL processes, databases, and data pipelines to handle large volumes of data and reduce processing times.

Job Summary We are looking for a skilled Data Engineer to join our team. The ideal candidate will have experience in building and optimizing data pipelines, architectures, and data sets. You will be responsible for creating and maintaining scalable data architectures, ensuring data availability, and enabling data-driven decision-making processes. This role requires a deep understanding of data management practices, data warehousing, and ETL processes.

Key Responsibilities Data Pipeline Development:
Design, construct, test, and maintain highly scalable data management systems. Develop and implement architectures that support the extraction, transformation, and loading ETL of data from various sources.

Data Integration:
Integrate structured and unstructured data from multiple data sources into a unified data system, ensuring data quality and consistency.

Data Warehousing:
Build and maintain data warehouses and data lakes to store and retrieve vast amounts of data efficiently. Optimize the performance of databases and queries to meet business needs.

Data Processing:
Implement data processing frameworks e.g., Hadoop, Spark to process large datasets in real-time or batch processing.

Automation and Monitoring:
Automate manual processes, optimize data delivery, and develop data monitoring systems to ensure data integrity and accuracy.

Collaboration:

Work closely with data scientists, analysts, and other stakeholders to understand data needs and provide technical solutions that meet business requirements.

Data Governance:
Ensure data governance policies are followed, including data security, data privacy, and compliance with regulations.

Performance Tuning:
Optimize the performance of ETL processes, databases, and data pipelines to handle large volumes of data and reduce processing times.

Skill Requirements Data Pipeline Development:
Design, construct, test, and maintain highly scalable data management systems. Develop and implement architectures that support the extraction, transformation, and loading ETL of data from various sources.

Data Integration:
Integrate structured and unstructured data from multiple data sources into a unified data system, ensuring data quality and consistency.

Data Warehousing:
Build and maintain data warehouses and data lakes to store and retrieve vast amounts of data efficiently. Optimize the performance of databases and queries to meet business needs.

Data Processing:
Implement data processing frameworks e.g., Hadoop, Spark to process large datasets in real-time or batch processing.

Automation and Monitoring:
Automate manual processes, optimize data delivery, and develop data monitoring systems to ensure data integrity and accuracy.

Collaboration:

Work closely with data scientists, analysts, and other stakeholders to understand data needs and provide technical solutions that meet business…